Learn data science, machine learning, and AI in Python. Master linear regression from theory to coding, and tackle real-world problems effectively.
This course is all about diving into the exciting world of data science and machine learning using Python. You’ll learn to build your own linear regression model and discover how these concepts apply to real-life scenarios. Fun times ahead, trust me!
Data science, machine learning, and artificial intelligence in Python for students and professionals
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.
This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python.
Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come. That's why it's a great introductory course if you're interested in taking your first steps in the fields of:
deep learning
machine learning
data science
statistics
In the first section, I will show you how to use 1-D linear regression to prove that Moore's Law is true.
What's that you say? Moore's Law is not linear?
You are correct! I will show you how linear regression can still be applied.
In the next section, we will extend 1-D linear regression to any-dimensional linear regression - in other words, how to create a machine learning model that can learn from multiple inputs.
We will apply multi-dimensional linear regression to predicting a patient's systolic blood pressure given their age and weight.
Finally, we will discuss some practical machine learning issues that you want to be mindful of when you perform data analysis, such as generalization, overfitting, train-test splits, and so on.
This course does not require any external materials. Everything needed (Python, and some Python libraries) can be obtained for FREE.
If you are a programmer and you want to enhance your coding abilities by learning about data science, then this course is for you. If you have a technical or mathematical background, and you want to know how to apply your skills as a software engineer or "hacker", this course may be useful.
This course focuses on "how to build and understand", not just "how to use". Anyone can learn to use an API in 15 minutes after reading some documentation. It's not about "remembering facts", it's about "seeing for yourself" via experimentation. It will teach you how to visualize what's happening in the model internally. If you want more than just a superficial look at machine learning models, this course is for you.
"If you can't implement it, you don't understand it"
Or as the great physicist Richard Feynman said: "What I cannot create, I do not understand".
My courses are the ONLY courses where you will learn how to implement machine learning algorithms from scratch
Other courses will teach you how to plug in your data into a library, but do you really need help with 3 lines of code?
After doing the same thing with 10 datasets, you realize you didn't learn 10 things. You learned 1 thing, and just repeated the same 3 lines of code 10 times...
Suggested Prerequisites:
calculus (taking derivatives)
matrix arithmetic
probability
Python coding: if/else, loops, lists, dicts, sets
Numpy coding: matrix and vector operations, loading a CSV file
WHATÂ ORDERÂ SHOULDÂ IÂ TAKEÂ YOURÂ COURSESÂ IN?:
Check out the lecture "Machine Learning and AIÂ Prerequisite Roadmap" (available in the FAQ of any of my courses, including the free Numpy course)
Derive and solve a linear regression model, and apply it appropriately to data science problems
Program your own version of a linear regression model in Python
Understand important foundations for OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion
12 Lectures
7 Lectures
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The Lazy Programmer is a seasoned online educator with an unwavering passion for sharing knowledge. With over 10 years of experience, he has revolutionized the field of data science and machine learning by captivating audiences worldwide through his comprehensive courses and tutorials.Equipped with a multidisciplinary background, the Lazy Programmer holds a remarkable duo of master's degrees. His first foray into...
Goof course
The introduction has been clear to differentiate between statistics and ML so that we as learners do not confuse the two.
I appreciate that.
Course content is very good and explanation is also perfect. I love the coding challenges and the way they make you think harder.
worst explanation of linear regression i have ever heard
As a CS student, I think this course is a must for anyone who wants to dive deep into Data Science. The theoretical and mathematical approach used (often forgotten in other courses, or even in college) gives you the understanding and strong foundation needed to become a real problem solver.
I really enjoyed the Lazy Programmer lectures. During the course I was already able to build 2 data analysis projects, by combining regression with my data. Very happy with the outcome.
There is a lot of really good information in this course. If you are rusty on multi-variable calculus, you may have to go through this a couple times and do some side work. But all of this is really important foundation for other classes to come.
The Topic is different and that I understand, hence I am just trying to understand the concept.
This is exactly what is needed ..... Amazing content to the point !
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Elena H.
I appreciate all the extra material